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# coding: utf-8
# # Choropleth Maps Exercise
# Pradeep K. Pant
# [Full Documentation Reference](
# ## Plotly Imports
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode,iplot
# Q1: World Power Consumption 2014
# Basic preparation
# Import pandas and read the csv file: 2014_World_Power_Consumption
import pandas as pd
df = pd.read_csv('2014_World_Power_Consumption')
# Check the head of the DataFrame.
# We need to create data and layout variable which contains a dict
data = dict(type='choropleth',
locations = df['Country'],
locationmode = 'country names',
z = df['Power Consumption KWH'],
text = df['Country'],
colorbar = {'title':'Power Consumption KWH'},
colorscale = 'Viridis',
reversescale = True
# Lets make a layout
layout = dict(title='2014 World Power Consumption',
geo = dict(showframe=False,projection={'type':'Mercator'}))
# Pass the data and layout and plot using iplot
choromap = go.Figure(data = [data],layout = layout)
# Q2: USA Choropleth
# Import the 2012_Election_Data csv file using pandas.
usadf = pd.read_csv('2012_Election_Data')
# Check the head of the DataFrame.
# Now create a plot that displays the Voting-Age Population (VAP) per state.
# First make data dict
data = dict(type='choropleth',
locations=usadf['State Abv'],
locationmode = 'USA-states',
z = usadf['Voting-Age Population (VAP)'],
text = usadf['State'],
colorbar = {'title':'Voting Age Polulation (VAP)'},
colorscale = 'Viridis',
reversescale = True)
# Make a nice layout to show all the USA states
layout = dict(title='2012 US Elections: Voting Age Population',
geo = dict(scope='usa', showlakes=True, lakecolor='rgb(85,173.240)'))
# Finally make plot using data and layout
choromap = go.Figure(data = [data],layout = layout)